# How are the values for the sex feature in sklearn Diabetes dataset obtained?

I'm just starting out with using sklearn for my own Machine Learning project and I'm using sklearn's built-in "Diabetes" dataset.

While performing data exploration on the features, I noticed something a bit confusing to me about the sex feature. Here's the hist plot: Now there are 2 things I do understand here:

1. The binary histogram makes sense, there are in this dataset 2 distinct 'sexes' of male and female.
2. Them being numerical also makes sense, as it appears all features in this dataset have already been 'normalized'.

What I don't understand is why the values are the way they are? (See below for what the values are)


>>> from sklearn import datasets
>>> features = diab_df['data']
>>> features.sex.unique()

array([ 0.05068012, -0.04464164])


How are these numbers derived? At first, I thought it could be some sort of stratified sampling, where if the true population distribution is say, 53% male, 47% female, then I'd maybe expect to see the values in this hist to be -0.47 & 0.53 or something?

• Have a look at the standardization procedure: "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times n_samples (i.e. the sum of squares of each column totals 1)." When you apply this to the un-standardized dataset you should get the standardized values as in given in the sklearn dataset. Sep 5, 2021 at 12:10

Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times n_samples (i.e. the sum of squares of each column totals 1). https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html
from sklearn import datasets